Abstract: A system for predicting and summarizing medical events from electronic health records includes a computer memory storing aggregated electronic health records from a multitude of patients of diverse age, health conditions, and demographics including medications, laboratory values, diagnoses, vital signs, and medical notes. The aggregated electronic health records are converted into a single standardized data structure format and ordered arrangement per patient, e.g., into a chronological order. A computer (or computer system) executes one or more deep learning models trained on the aggregated health records to predict one or more future clinical events and summarize pertinent past medical events related to the predicted events on an input electronic health record of a patient having the standardized data structure format and ordered into a chronological order.

Abstract: The present disclosure is directed to systems and method to perform improved detection of out-of-distribution (OOD) inputs. In particular, current deep generative model-based approaches for OOD detection are significantly negatively affected by and struggle to distinguish population level background statistics from semantic content relevant to the in-distribution examples. In fact, such approaches have even been experimentally observed to assign higher likelihood to OOD inputs, which is opposite to the desired behavior. To resolve this problem, the present disclosure proposes a likelihood ratio method for deep generative models which effectively corrects for these confounding background statistics.

Abstract: A computer-implemented technique can include obtaining a vector-based language model associating elements of an unlabeled corpus that have similar meanings, training a machine-learning classifier using the vector-based language model and a labeled corpus of text that has been annotated as having a particular level of abusiveness, obtaining a text, determining a prediction for the text using the machine-learning classifier, the prediction being indicative of a level of abusiveness of the text, and based on the level of abusiveness of the text, selectively outputting a recommended action with respect to the text.

Abstract: Systems and methods are provided for organizing messages. A plurality of messages is communicated to a user with a designation of the message category of each respective message in the plurality of messages. The plurality of messages includes, for each respective message category in a plurality of message categories, at least one message in the respective message category. A user selection of a first message in the plurality of messages is received, whereupon a subset of the plurality of messages similar to the first message is selected based upon respective comparisons of continuous vector representations of (i) a set of words in the first message and (ii) a corresponding set of words in messages in the subset of messages. An identification of each respective message in the subset of messages is then displayed.

Abstract: Systems and methods are provided for organizing messages. A plurality of messages is communicated to a user with a designation of the message category of each respective message in the plurality of messages. The plurality of messages includes, for each respective message category in a plurality of message categories, at least one message in the respective message category. A user selection of a first message in the plurality of messages is received, whereupon a subset of the plurality of messages similar to the first message is selected based upon respective comparisons of continuous vector representations of (i) a set of words in the first message and (ii) a corresponding set of words in messages in the subset of messages. An identification of each respective message in the subset of messages is then displayed.